MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Table of Contents

Title: MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

Authors: Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun

Published: Oct 18, 2022

Link: https://arxiv.org/abs/2210.10163

Summary (Generated by Microsoft Copilot):

Introduction:

  • MedCLIP addresses the challenge of limited paired medical image-text datasets by decoupling images and texts for multimodal contrastive learning.

Challenges:

  • Data Insufficiency: Medical image-text datasets are much smaller than general datasets.
  • False Negatives: Separate patient images and reports with similar semantics are wrongly treated as negatives.

Methods:

  • Decoupling Images and Texts: Utilizes unpaired datasets to scale training data.
  • Semantic Matching Loss: Uses medical knowledge to eliminate false negatives.

Novelties:

  • Combines unpaired images and texts to expand training data.
  • Introduces a semantic matching loss based on medical knowledge.

Results:

  • Outperforms state-of-the-art methods in zero-shot prediction, supervised classification, and image-text retrieval.

Performances:

  • Achieves superior accuracy with significantly less pre-training data compared to other methods.

Limitations:

  • Challenges with incorrect semantic tags and missing detection of negation or uncertainty phrases.

Discussion:

  • MedCLIP demonstrates high data efficiency and transferability to various downstream tasks, supporting foundational models for medical diagnosis.